My expertise in statistical analysis spans advanced methodologies, including hypothesis testing, regression modelling, and mltivariate analysis, applied to both behvaioural and neuropsychological research. In a study on social behvaiour in Drosophila melanogaster, I employed two-way ANOVA to assess the impact of genetic variation has on socialization patterns and sleep cycles, demonstrating how environemtn and genetic factors contribute to behvaioural regulation. I used multivariate analysis to study the effects of sensorimotor confusion on auditory perception. Throughout these projects, I have leveraged statistical software, including SPSS, R, JASP, and Python (Statsmodels, SciPy), to conduct rigorous data preprocessing, hypothesis testing, and predictive modeling, ensuring reproducibility and methodological rigor in my analyses.
My expertise in neuroimaging focuses on EEG signal processing, event-related potential (ERP) analysis, and artifact rejection techniques for both EEG and fNIRS data. In my major thesis, I conducted an EEG-based study on auditory hallucinations, examining how sensorimotor conflicts influence auditory processing. This project involved the use of a robotic system to induce mismatched sensory feedback, allowing for the investigation of neural correlates underlying perceptual distortions. Additionally, I performed a meta-analysis of ERP studies to synthesize findings on response inhibition and impulsivity, integrating data from multiple sources to provide a comprehensive understanding of cognitive control mechanisms. Beyond empirical research, I have also contributed to methodological advancements by developing Python-based automation pipelines for EEG preprocessing. These scripts streamlined bandpass filtering, independent component analysis (ICA), and artifact detection, significantly improving the efficiency and reproducibility of EEG data analysis. My proficiency in neuroimaging tools such as MNE-Python, EEGLAB (MATLAB), and FieldTrip has enabled me to conduct sophisticated neural data analyses while ensuring methodological rigor and reproducibility in cognitive neuroscience research.
My expertise in programming and data analysis primarily revolves around Python, with a strong focus on data preprocessing, EEG/ERP signal processing, and statistical modeling. In the context of neuroimaging research, I developed an automated EEG/ERP preprocessing pipeline, implementing custom Python scripts to clean, segment, and analyze neural data. This approach minimized manual errors, improved reproducibility, and facilitated large-scale data processing. Additionally, my work in behavioral research involved writing Python scripts to analyze false positive rates, accuracy, precision, and response times in cognitive tasks, ensuring robust statistical evaluation of experimental results. Beyond human neuroscience, I applied computational methodologies to behavioral tracking in Drosophila melanogaster, where I designed custom algorithms to process over 1,638 hours of movement data. These algorithms extracted complex behavioral patterns, including social interaction metrics and locomotion differences, contributing to a deeper understanding of genetic influences on behavior. This not only benefitted me but also contributed to published research. My technical proficiency extends to working with scientific computing libraries such as NumPy, SciPy, Pandas, and Matplotlib for statistical modeling and visualization, as well as specialized neuroimaging tools like MNE-Python for EEG analysis and TREX for drosophila behavior tracking. Through these projects, I have demonstrated the ability to integrate computational techniques into neuroscience research, improving data processing efficiency and enhancing analytical rigor.
My expertise in behavioral research encompasses cognitive task design, psychometric assessments, and behavioral tracking across both human and animal models. In clinical psychology, I administered and analyzed personality and cognitive function tests for over 150 participants in a study examining the relationship between substance abuse, emotional regulation, and alexithymia. This work involved rigorous psychometric evaluation to differentiate clinical from non-clinical populations. In the domain of animal behavior, I designed and implemented a large-scale tracking study on Drosophila melanogaster, creating a comprehensive social interaction matrix from 68 days of continuous movement data. This project provided insights into the effects of genetic variation on social behavior and sleep patterns. Additionally, I conducted Signal Detection Test (SDT) where a robot was used to artificially induce sensorimotor confusion and particiapnts' accuracy, precision and false positive rate were measured using EEG-based. These experiments required precise stimulus presentation, real-time behavioral recording, and advanced data analysis. My methodological proficiency includes the use of TREX for automated fly tracking, PsychoPy for cognitive task programming, and Python for stimulus presentation. By integrating psychometric testing with experimental behavioral paradigms, I have contributed to a multidisciplinary understanding of cognition, emotion, and behavior.
Effective data visualization is essential for translating complex research findings into clear, accessible insights. My expertise lies in creating high-quality scientific figures, graphical abstracts, and data-driven visualizations that enhance the interpretability and impact of research. I have designed neuroscience diagrams that depict EEG signal processing pipelines and sensorimotor integration models, aiding in the conceptualization of intricate neural mechanisms.
Beyond static figures, I have utilized Python’s Seaborn and Matplotlib libraries to generate heatmaps and data visualizations for behavioral and neuroimaging research. This includes mapping Drosophila movement data across 1,638+ hours of tracking and visualizing EEG results to highlight signal patterns relevant to cognitive processing. My proficiency in visualization tools such as BioRender, Canva, and Paint3D allows me to create publication-ready figures while ensuring accessibility for both academic and general audiences. Through these visualizations, I contribute to effective scientific communication, making complex data more comprehensible and engaging.
My work in research automation and methodological innovation has focused on developing computational tools to enhance data analysis, improve reproducibility, and streamline experimental workflows. In neuroimaging research, I designed a Python-based automated analysis pipeline for EEG signal processing, enabling faster and more scalable data preprocessing, artifact rejection, and statistical analysis. In behavioral neuroscience, I developed a Drosophila social analysis toolkit that introduced a novel, non-intrusive method for extracting social interaction data from long-term behavioral recordings. This innovation significantly improved research accuracy and efficiency. Additionally, in cognitive neuroscience, I created custom scripts to automate neurocognitive task analyses, facilitating the extraction of reaction times, error rates, and neural correlates in cognitive experiments.
Beyond research-specific automation, I have contributed practical tools to optimize workflows for researchers. I developed an NPZ-to-CSV converter to simplify data handling for Drosophila movement tracking in TREX, making it easier for my lab and other researchers to process and analyze behavioral datasets. Furthermore, I designed a schematic for the optimal placement of electrooculography (EOG) electrodes to facilitate a clearer understanding of electrode positioning. This schematic serves as a practical reference for both new and experienced researchers during EEG experiments, ensuring accurate signal acquisition. My methodological contributions emphasize research automation, efficiency, and accessibility, leveraging tools such as Python, GitHub, and the Open Science Framework to enhance data analysis pipelines and experimental reproducibility.
My dedication to academic excellence, research, and community engagement has been recognized through several awards and merit certificates. I was awarded the Best Intern Award at Unique Psychological Society for outstanding performance during my clinical psychology internship, where I demonstrated strong diagnostic, assessment, and therapeutic skills. Additionally, I received a Certificate of Participation for serving as a reporter at the NDIM Model United Nations (MUN) 2018, reflecting my ability to critically analyze and communicate complex information effectively.
Beyond academic and research contributions, my commitment to social work has been acknowledged through two Letters of Appreciation from the Delhi Municipal Community and Unique Psychological Services (UPS) for exceptional performance in community-based psychological interventions. These recognitions highlight my interdisciplinary expertise, leadership, and ability to apply psychological principles in diverse professional and societal contexts.
As a certified cadet in the National Cadet Corps (NCC), I successfully completed three years of basic military training, earning both the “B” and “C” certificates. This rigorous training encompassed small arms handling, drill discipline, and field exercises, fostering a high level of resilience, adaptability, and mental endurance.
My participation in two Combined Annual Training Camps (CATC) provided intensive leadership and endurance training, further strengthening my ability to work efficiently under high-pressure conditions. The structured environment of NCC instilled in me a no-quit mindset, emphasizing teamwork, time management, and discipline—qualities that seamlessly translate into academic research and professional settings. These experiences have equipped me with the ability to remain composed in demanding situations while effectively coordinating with teams to achieve collective goals.
My leadership experience spans academic, clinical, and student governance roles, demonstrating my ability to manage teams, advocate for student interests, and effectively communicate complex concepts across disciplines.
At DeepOrigins, I led a team of 15+ clinical psychology interns, overseeing psychotherapy sessions, reviewing case reports, and ensuring adherence to ethical and professional standards. This role required strong organizational skills, mentorship, and the ability to delegate responsibilities efficiently, fostering a collaborative and structured learning environment.
As a Program Committee and Student Council Member at the University of Groningen, I actively contributed to curriculum development, represented student concerns in faculty meetings, and advocated for enhanced student support systems and mental health resources. This role required strategic problem-solving, negotiation, and collaboration with faculty and peers to improve the academic experience for students.
Beyond research and governance, I also engaged in interdisciplinary teaching as a Guest Lecturer on Animation & Psychology at Narsee Monjee College of Commerce & Economics. I designed and delivered an interactive session exploring the intersection of visual perception, cognitive science, and animation, making complex neuroscience concepts accessible to VFX students.
These experiences have strengthened my public speaking, mentorship, and leadership skills, allowing me to effectively guide research teams, advocate for institutional improvements, and translate academic knowledge into engaging, real-world applications for diverse audiences.
My academic and research journey has equipped me with a diverse set of soft skills that are essential for success in interdisciplinary research and leadership roles.
Project management has been a key aspect of my work, as I have successfully organized and executed multiple research projects while leading teams across various domains. Whether managing EEG studies, behavioral research, or computational modeling projects, I have ensured the timely completion of tasks, optimized workflows, and maintained high methodological rigor.
Strong public speaking and communication skills have allowed me to effectively present research findings, engage in student council debates, and deliver guest lectures. I am experienced in tailoring complex neuroscience concepts for different audiences, whether in academic conferences, interdisciplinary discussions, or public engagement initiatives.
My ability to lead and collaborate within research teams, internships, and student organizations has been demonstrated through various leadership roles. From guiding clinical psychology interns to representing students on academic committees, I have effectively coordinated, delegated, and facilitated teamwork to achieve collective goals.
Adaptability and problem-solving have been critical throughout my research career, especially in troubleshooting experimental challenges, managing technical setbacks, and mentoring teams under pressure. My ability to remain composed in high-stakes situations has enabled me to devise innovative solutions and maintain the integrity of research processes.
These skills position me as a well-rounded researcher and leader, excelling in both independent problem-solving and collaborative environments. I am well-prepared to take on leadership roles in PhD projects and academia, contributing effectively to complex research initiatives and interdisciplinary collaborations.